Method and apparatus for vital signs measurement

ABSTRACT

A method of monitoring changes in oxygen saturation of a subject by analysing a three colour channel video image of the exposed skin of the subject. Within each colour channel a normalised signal obtained by dividing the intensity signal by its mean value, and the normalised signals are averaged across plural regions of interest within the exposed skin area image of the subject. Regions of interest are selected on the basis of the signal-to-noise ratios for the heart rate and breathing rate components. A single representative waveform for each colour channel is obtained by signal averaging and the ratio of the amplitudes of the representative waveforms from two different colour channels, e.g. blue and red, is taken. The changes in the ratio of amplitudes is output as a measure of changes in blood oxygen saturation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a 371 U.S. National Stage of InternationalApplication No. PCT/GB2016/051628, filed Jun. 2, 2016, which claims thebenefit of British Patent Application No. 1509809.8, filed Jun. 5, 2015.The entire disclosures of the above applications are incorporated hereinby reference.

The present invention relates to a method and apparatus for vital signsmeasurement and in particular to the estimation of blood oxygensaturation of a subject.

The measurement of vital signs such as heart rate, breathing rate andblood oxygen saturation is of critical importance in the medical field,but also finds application in other fields such as sports performancemonitoring. Traditionally such vital signs have been measured by sensorsplaced in contact with the subject but more recently significantinterest has developed in non-contact vital signs monitoring. Inparticular, the ready availability of reasonably high quality videocameras and their provision in many common devices such as tabletcomputers and smartphones has prompted efforts to derive measurements ofvital signs such as heart rate and breathing rate from a video image ofthe exposed skin of a subject. WO-A2-2013/027027, for example, disclosesderiving measurements of the heart rate and breathing rate from aphotoplethysmographic image (PPGi) formed by the remote reflectancephotoplethysmography (rPPG) signal. The rPPG signal is a variation inreflectance of light at certain wavelengths as the volume of blood inthe skin capillaries varies with the cardiac cycle. Although invisibleto normal sight, the skin can effectively be regarded as pulsing morered and less red with each heat beat. This change can be detected in thestandard RGB colour video image of the skin taken with a normal videocamera such as a webcam.

Blood oxygen saturation (SpO2), is a measure of the relativeconcentration of oxygenated haemoglobin in the blood with respect to thetotal haemoglobin. As a result, oxygen saturation is a very powerfulmeasure for the assessment of lung function and the respiratory systemitself. The clinical gold standard for precise measurement of oxygensaturation involves invasive blood gas analysis, but this has largelybeen supplanted by pulse oximetry which is the current standard for SpO2measurement. Pulse oximetry takes advantage of the facts that:oxygenated haemoglobin and deoxygenated haemoglobin absorb differentlyat different wavelengths, that arterial blood is mostly pulsatile innature, and that an optical window exists in the far visible andshort-wave infrared in water. These factors allow SpO2 to be measured byilluminating tissue using red and infrared LED light sources in contactwith the skin, measuring the reflected light intensity, and calculatingthe amplitude of the pulsatile component (AC) with respect to thebaseline (DC) for both light sources. A ratio of ratios is thenevaluated, usually the ratio of AC to DC in the red channel divided bythe ratio of AC to DC in the infrared channel. This ratio of ratios iscorrelated to the blood oxygen saturation and can be converted into ameasurement of SpO2 using a look-up table.

Although it has been recognised that the spatially-averaged RGB colourof skin in a PPGi video signal is correlated with arterial oxygensaturation, obtaining a reliable measurement of oxygen saturation, oreven of the change in oxygen saturation, has not been possible becauseof the number of confounding factors and sources of noise. For example,the video signal can be affected by specular reflections from the skinof the subject (specularly reflected light not having interacted withthe tissue of the subject), and any change in the geometrical alignmentof the camera, skin and light source (such as, for example, a slightmovement or change in orientation of the subject) causes changes in thevideo signal which are not related to changes in blood oxygensaturation. Changes in intensity of the ambient light also affect thesignal. For this reason non-contact PPGi methods of vital signsmonitoring have concentrated on obtaining better estimates of heart rateand breathing rate.

The present invention provides a method and apparatus forphotoplethysmographic image analysis (PPGi) which allows oxygensaturation changes to be tracked with high accuracy over time usingbroadband lighting and standard RGB video cameras. Although not directlyproviding an absolute measure of oxygen saturation, the method of theinvention accurately tracks changes in oxygen saturation and is robustto small movements of the subject and consistent over several hours ofrecording.

One aspect of the invention provides a method of determining changes inblood oxygen saturation of a subject comprising the steps of: obtaininga video image of an area of exposed skin of the subject, the video imagecomprising signals representing intensity in at least two differentcolour channels; defining in the image a plurality of regions ofinterest in said area of exposed skin of the subject; determining asignal to noise ratio of a heart rate or breathing rate frequencycomponent of one of the colour channel signals for each region ofinterest; determining whether to reject or not reject regions ofinterest based on the determined signal to noise ratio of a heart rateor breathing rate frequency component or both; processing the colourchannel signals from non-rejected regions of interest by: normalisingthe signal in each of the colour channels by dividing each of thesignals by its baseline component; determining the ratio of theamplitudes of the normalised signals from two of the colour channelsignals; and outputting changes over time in the ratio as representingchanges in blood oxygen saturation of the subject.

The video image is preferably obtained using a standard RGB video camerasuch as a webcam, delivering a standard three colour channel (RGB) videosignal.

Preferably the method further comprises the step of averaging each thenormalised signals within each colour channel before calculating theratio of amplitudes.

Preferably the processing steps of normalising and determining the ratioof amplitudes are performed on a series of overlapping temporal windowsof the signal; for example each window can be 10-15 seconds long andmoved for each processing cycle by 1-5 seconds. Thus a new measurementis output for every step, representing an average over the previouswindow length.

To normalise the signal in each of the colour channels, each signal isdivided by its base line component, the baseline component beingrepresentative of the DC level of the signal in the window. For examplethe baseline component can be a temporal average over the window, forexample the mean value of the signal over the window, or it could be atimeseries made up of the interpolated values of the per-pulse averageor the troughs of the envelope of the signal. Preferably the two colourchannel signals are the red and blue channels as the red channel is mostaffected by changes in oxygen saturation and the blue channel leastaffected, though the combination of green and red channels can be used.

Preferably the step of averaging each of the normalised signalscomprises averaging together signals obtained from each of the pluralityof regions of interest within the exposed area of the skin of thesubject, thus providing spatial averaging. Thus an area of exposed skinof the subject may be found in the video image, for example by knowntechniques such as facial recognition, or recognising skin colour, orlooking for areas of the image with a strong pulsatile component at theheart rate or in the physiologically possible range for heart rate, andthe area is divided into the plural regions of interest which may becontiguous square or rectangular regions. One way of finding an area ofexposed skin with a strong pulsatile component is to search for largeareas where a signal to noise ratio function for the heart rate ismaximised. This may be performed, for example, on the green channel ofthe RGB signal.

Preferably within each region of interest an estimate of the strength ofthe heart rate frequency component is obtained, for example by measuringthe strength of the signal-to-noise ratio for a heart rate frequencycomponent of one of the colour channels, and the contribution of thesignal from that region to the average of the normalised signals isweighted according to the strength of the heart rate signal. Thesignal-to-noise ratio of the heart rate frequency component may be foundin the red colour channel.

Preferably regions of interest are excluded from the determination if anumber of conditions related to the heart rate frequency component,breathing rate frequency component and phase of the heart rate frequencycomponent are met. Placing these conditions on inclusion helps ineliminating from the calculation regions of interest which are stronglyaffected by specular reflections. For example, a condition on the heartrate frequency component may be that its signal to noise ratio shouldexceed a predetermined threshold. A condition on the breathing ratefrequency component may be that its signal to noise ratio is below apredetermined threshold (this allows the exclusion of regions which areaffected by movement). Examining the phase of the heart rate frequencycomponent, and in particular comparing the phase of the heart ratefrequency component in the green channel from each region of interestwith the phase of the heart rate frequency component averaged over allregions of interest additionally helps in excluding regions of interestwhich are affected by movement as the heart rate should be reasonably inphase (allowing for the pulse transit time) over a local area consistingof contiguous regions of interest.

Preferably, having obtained a normalised signal from the area of theexposed skin, the technique of signal averaging can be applied todetermine a single representative waveform whose amplitude is then usedin the ratio of ratios calculation. This is a self-referential timeaveraging technique. Preferably the signal averaging is performed bydetecting a series of successive peaks in a time-windowed portion of thesignal and selecting sections of the signal extending from apredetermined time before each peak to a predetermined time after it,and averaging together the selected sections. The section may extendfrom a time two thirds of the signal period before the peak to twothirds of the signal period after the peak, each section thus includingslightly more than one signal period. This results in one average,representative waveform for the particular time-windowed portion of thesignal. The amplitude of this waveform can be taken as representing thesignal amplitude for that portion of signal.

Instead of spatially averaging the signals from the different regions ofinterest together, and then performing temporal signal averaging on theresult to obtain a representative waveform, these steps may be reversed.Thus within each region of interest temporal signal averaging may beperformed to obtain a representative waveform for each region ofinterest, and then representative waveforms from the different regionsof interest can themselves be averaged together. Thus the techniques ofspatial averaging over the different regions of interest and signalaveraging within the time domain can be applied in either order.

The invention extends to a system or apparatus for determining changesin oxygen saturation of a subject, the system or apparatus including avideo camera for obtaining the video image, a signal processor forperforming the signal processing steps of the method and a display foroutputting the changes over time in the ratio of amplitudes of theaverage normalised signals.

The signal processing steps of the invention may be applied to obtainingother vital signs measurements, such as heart rate and breathing rate,from RGB video images. Thus the effect of reflections and illuminationchanges can be reduced by including or rejecting regions of interest inthe signal processing on the basis of the signal to noise ratio of aheart rate and/or breathing rate frequency component, and/or a phasecomparison of a heart rate frequency signal from one region of interestwith an average from all regions of interest.

Thus another aspect of the invention provides a method of determining avital sign (such as heart rate or breathing rate) of a subjectcomprising the steps of: obtaining a video image of an area of exposedskin of the subject, the video image comprising signals representingintensity in at least two different colour channels; defining in theimage a plurality of regions of interest in said area of exposed skin ofthe subject; determining a signal to noise ratio of a heart rate orbreathing rate frequency component of one of the colour channel signalsfor each region of interest; determining whether to reject or not rejectregions of interest based on the determined signal to noise ratio of aheart rate or breathing rate frequency component or both; processing thecolour channel signals from non-rejected regions of interest todetermine the vital sign. In addition to, or instead of, therejection/non-rejection based on signal to noise ratio, a determinationof whether to reject or not may be based on a phase comparison of aheart rate frequency signal from one region of interest with an averagefrom all regions of interest.

This aspect of the invention also provides a corresponding apparatus fordetermining a vital sign (such as heart rate or breathing rate) of asubject, comprising a video camera for obtaining the video image, asignal processor for performing the signal processing steps of themethod and a display for outputting the determined vital sign.

The method of the invention may be embodied in a computer program andthe invention extends to such a computer program and to a computerreadable medium carrying such a program.

The invention will be further described by way of example with referenceto the accompanying drawings in which:—

FIG. 1 schematically illustrates the system of the invention;

FIG. 2 is a flow diagram illustrating the steps of one embodiment of theinvention;

FIGS. 3(a) and 3(b) schematically illustrate signal averaging; and

FIGS. 4(a) and 4(b) compare the results of monitoring oxygen saturationwith an embodiment of the invention to results obtained using afinger-probe pulse oximeter.

In the context of broad-band illumination and the use of RGB sensors,the time-series S^(i) _(c) of intensity values recorded by a camera fromany given region of skin i for any given colour channel c may bedecomposed into two parts: the baseline, or DC component, due to theresidual blood present in the tissue at all times, and the pulsatile,heart-rate synchronous signal due to the change in colour as the bloodflows in and out of the skin:S _(c) ^(i)(t)∝DC _(c) ^(i)(t)+AC _(c) ^(i)(t)  (1)

This assumes that all light is reflected diffusively and ignores anycomponent due to specular reflection (we will see below that the signalprocessing used in the invention makes this a reasonable assumption). Wewill further assume that the whole timeseries S^(i) _(c) (t) will beaffected by only the following variable factors:

-   -   i) The intensity of the light reaching the skin at i    -   ii) The vascular volume at i    -   iii) The oxygen saturation.

Other factors that will affect the absorption of light in the skin, suchas melanin, and the spectral distribution of the light source at i, areassumed to be constant. The method of the invention allows theelimination of the effects of light intensity and vascular volumechanges so as to determine changes in oxygen saturation. Of the threefactors i) to iii) above, the oxygen saturation is assumed to belocally-invariant, whereas the intensity of the light is allowed to varylocally (for example through geometrical effects such as shadowing), andthe vascular volume is also allowed to vary locally (reflectinganatomical variation in the vasculature of the skin). Under theseassumptions the relationship between the recorded intensity time seriesS^(i) _(c) from a region i, the spectrally-invariant light intensityI^(i) _(c) (t) reaching the region i, may be written as:S _(c) ^(i)(t)=I _(c) ^(i)(t)[DC _(c) ^(i)(t)+AC _(c) ^(i)(t)]  (2)

Using this model, the normalisation of the signal by the DC componentwill lead to an elimination of the effect of the local light intensityI^(i) _(c) (t):

$\begin{matrix}\begin{matrix}{{S_{c}^{i^{\prime}}(t)} = \frac{{I_{c}^{i}(t)}\left\lbrack {{{DC}_{c}^{i}(t)} + {{AC}_{c}^{i}(t)}} \right\rbrack}{{I_{c}^{i}(t)}{{DC}_{c}^{i}(t)}}} \\{= {1 + \frac{{AC}_{c}^{i}(t)}{\;{{DC}_{c}^{i}(t)}}}}\end{matrix} & (3)\end{matrix}$

There are however other factors, such as local blood volume increases,that come into play with respect to the normalised AC component that arenot necessarily eliminated by the normalisation itself. To eliminatethese, the ratio of ratios R^(i)(t for two colour channels 1 and 2 istaken, under the assumption that all changes that are not due tooxygenation (and therefore cause a change in colour), will beproportional across the channels:

$\begin{matrix}{{R^{i}(t)} = {\frac{{s_{1}^{i^{\prime}}(t)} - 1}{{s_{2}^{i^{\prime}}(t)} - 1} = \frac{\frac{{k^{i}(t)}{{AC}_{1}^{i}(t)}}{{DC}_{1}^{i}(t)}}{\frac{{k^{i}(t)}{{AC}_{2}^{i}(t)}}{{DC}_{2}^{i}(t)}}}} & (4)\end{matrix}$

FIG. 1 schematically illustrates the system of the invention. A human(or animal) subject 1 is in the field of vision of an RGB video camera 3whose three colour channel output is fed to a signal processor 5 andresults are displayed on a display 7. The processor 5 identifies andanalyses signals from one or more exposed areas of skin 10, 12 on thesubject 1 and, as explained in more detail below, each of these exposedareas 10, 12 is itself divided into plural regions of interest.

The processing of the video signals by the processor 5 will be describedwith reference to the flowchart of FIG. 2.

Following starting of the processing at step 100, during aninitialization step 101 an area of exposed skin is identified. This maybe done either by applying specific prior knowledge about the scene (forexample by face-detection if a face is known to be in the image), or bysimply doing a search using a very large search area for the position atwhich the result of the SNR function for heart rate (SNR_(HR)) ismaximised, where the SNR function is defined as:

$\begin{matrix}\begin{matrix}{{SNR} = {{SNR}\left\{ {x(t)} \right\}_{a}^{b}}} \\{= {10\;{\log\left( \frac{\int{V{{F\left\{ {x(t)} \right\}}}^{2}{df}}}{\int{\left( {1 - V} \right){{F\left\{ {x(t)} \right\}}}^{2}{df}}} \right)}}}\end{matrix} & (5)\end{matrix}$

for a detrended and appropriately filtered timeseries x(t), its Fouriertransform F{x(t)}, and a double-step function V(ƒ) defined by theconvolution:V(ƒ)=[δ(ƒ−{circumflex over (ƒ)})+δ(ƒ−2{circumflex over (ƒ)})]*Π(±ƒ_(h))

centred on the fundamental frequency of interest {circumflex over (ƒ)}and its first harmonic (e.g. {circumflex over (ƒ)}={circumflex over(ƒ)}_(HR) for heart rate), with δ as the Dirac delta function, and Π asthe rect function of half-width ƒ_(h).

For the purpose of initialisation, x(t) is taken as the green signalover a period of 12 seconds, and the double-step function for the heartrate SNR is constructed with ƒ_(HR)=1.4 Hz and ƒ_(h)=0.7 Hz so as tocover the entire span of the expected physiological heart rate range.

Once a search area in which the totality of the skin to be image isincluded has been defined in step 102, the area is subdivided into Ncontiguous n by n pixel regions of interest i (n=40 for example). Thesize of the region of interest is set depending on the camera, lightingand physiology of the subject so that the region is as small as possiblewhile still giving a detectable heart rate signal. Taking 12 secondwindows slid by 1 second at a time, crude estimates for the heart rate,{circumflex over (ƒ)}_(HR), and the breathing rate, {circumflex over(ƒ)}_(BR), are found. The heart rate estimate is found by taking theaverage of all the signals resulting from the per-frame spatial averageof the green channel, then detrending and high-pass filtering thisaverage prior to finding the peak of the Fourier Transform. Thebreathing rate estimate is instead found by taking the average of thepower spectral density (PSD) of the detrended blue channel in thefrequency domain across all regions of interest and then searching for apeak present in the expected physiological range (between 0.1 and 0.7 Hzcorresponding to 6 to 42 breaths per minute). The differences incalculating the breathing rate and the heart rate are due to the factthat the relative phase shift between the heart rate signal, that isprevalently due to colour changes as a result of the inflow and outflowof blood during the cardiac cycle, estimated in two different regions ofinterest from the plurality of regions of interest, is uniquelydetermined by the pulse transit time between the two regions. The phaseshift caused by the pulse transit time between the two regions isexpected to be far smaller than π/2 radians. The breathing rate signals,on the other hand, are caused by changes in colour due to movement, andso can be either in phase or in antiphase as this will solely depend onthe relative intensity of the pixels in the region of interest throughtime, and a temporal average would in fact minimise the breathing ratesignal.

The spatial averages across the 12 second windows are then calculatedfor each of the channels of the N regions of interest i to reduce thethree 2D plus time colour channel signals from each region to three 1Dsignals. The heart rate SNR function (5) as above is then applied toeach of the N regions of interest using the red channel only and fixingthe frequency limits a and b of the function at a=0.7 Hz and b=2.4 Hz,and ƒ_(h) is taken to be the quantisation limit of the FFT applied—e.g.0.7 Hz. A breathing rate SNR function as defined in Equation 5 is alsoapplied to each of the N regions of interest using the blue channel onlyand fixing the frequency limits a=0.1 Hz and b=0.7 Hz and the half-widthof the breathing rate rect function ƒ_(h) is once more the quantisationlimit of the FFT

The results of the heart rate and breathing rate SNR functions serve tocreate a logical inclusion function L^(i) that determines in step 105whether the region of interest will be used in further calculations forthat window or will be rejected. This serves to eliminate oscillationsin the signal caused by specular reflections because the inclusionfunction serves to introduce a degree of confidence that the signal fromthe selected region of interest is from a colour change only. In fact,for a time series that contains only a pulsatility due to a true PPGskin colour change, we would expect a high result for the heart rateSNR, but a low result for the breathing rate SNR. This is becausebreathing rate is mostly associated with movement, and any region ofinterest that has a high breathing rate SNR will therefore have somecomponent (whether a physical feature or a specular reflection) that ismoving and thus does not fit the model's assumptions. In addition tothis, a further condition is imposed: the heart rate component of thegreen channel of the region interest needs to be in phase with the greenheart rate signal derived for the whole search area. This conditionstems from the understanding that only movement-induced pulsations canbe in phase or antiphase, and is determined by meeting the conditionp^(i)=1 where:

$P^{i} = \left\{ \begin{matrix}{1,} & {{{if}\mspace{14mu}{{{\varnothing_{g}^{i}\left( {\hat{f}}_{HR} \right)} - {\varnothing_{G}\left( {\hat{f}}_{HR} \right)}}}} < \frac{\pi}{2}} \\{0,} & {otherwise}\end{matrix} \right.$where Ø_(g) ^(i) ({circumflex over (ƒ)}_(HR)) is the phase of the heartrate frequency component of the green signal of the region of interest iconsidered, and Ø_(G)({circumflex over (ƒ)}_(HR)) is the phase of theheart rate frequency component of the green signal averaged over theentire area. A phase difference of π/2 is chosen here because this givesthe clearest demarcation between phase estimates that can be said to bein phase (but for a phase shift caused by a delay due to the pulsetransit time) and the case for which two phase estimates are exactly inantiphase. The overall logical inclusion function is then given by:L ^(i)=(SNR _(HR) ^(i) >SNR _(HR) ^(thresh))∩(SNR _(BR) ^(i) <SNR _(BR)^(thresh))∩P ^(i)with SNR_(HR) ^(thresh) and SNR_(BR) ^(thresh) determined by the initialconditions of the video (these will depend on skin colour, lightintensity, light spectrum and distance from the camera). The thresholdscan, for example, be taken as the mean plus one standard deviation ofthe SNRs in each of the regions of interest over the first stablewindow. In Step 106, the normalised amplitude is determined for eachcolour channel as per Equation 4 in all M regions of interest that meetthe condition L^(i)=1. As the method depends on multiple regions toreduce the measurement error, a minimum number of regions M≥3 is set ateach iteration or the window is rejected. The DC component is taken asthe time-average of the window for each colour channel and each regionof interest individually and the AC component is taken to be theresidual of the original timeseries after the DC component has beensubtracted out. A weighted average of all the M normalised amplitudes isthen taken in step 107 for each of the channels, in which the region ofinterest weightings ω_(i) are a function of the heart rate SNR, suchthat:

${\omega_{i} = \frac{{SRN}_{HR}^{i}}{\sum^{M}{SNR}_{HR}^{i}}},\left\{ {{i\text{:}\mspace{14mu} L^{i}} = 1} \right\}$

The weighting introduces an additional degree of belief in each regionof interest, favouring regions of interest that have a high heart ratesignal-to-noise ratio as these are more likely to correspond to theideal surfaces that are considered in the theoretical model.

Finally, in step 108 a self-referential signal averaging procedure isapplied to the averaged waveforms. This is done by taking the greenchannel averaged waveform, high-pass filtering it and finding thepositions of the peaks in the waveform (the green channel is usedbecause it has a high signal-to-noise ratio). In each of the threecolour channels all samples around the peaks

$\pm \frac{2}{3f_{HR}}$in the averaged waveforms are then averaged together to obtain a singlerepresentative waveform for the entire 12-second period for eachchannel. The amplitude of the waveform is taken as the normalisedamplitude for that channel and the ratio of the blue normalisedamplitude with respect to the normalised amplitude of the red channel isthen taken as the ratio of ratios in step 109. The ratio of thenormalised blue amplitude divided by the normalised red amplitudeemerging from step 109, or its logarithm, is then output in step 110 asrepresentative of the oxygen saturation and displayed on display 7.Steps 103 to 110 are then repeated until there is a significant movementof the subject outside of the search area as checked at step 111, atwhich point the algorithm is halted until there is a period of nomovement and then reinitialised in step 112 and restarted.

FIGS. 3(a) and (b) schematically illustrate a signal averaging processapplied to a waveform from one colour channel. In FIG. 3(a), sections 31of each of the successive waveforms centred on the peaks 33 are taken,the peaks are aligned and the waveforms averaged to produce a singlerepresentative shape waveform for that signal as shown in FIG. 3(b). Theamplitude 35 of the representative waveform is taken as the amplitudefor that signal.

Although in FIG. 2 and as explained above, the 12-second sections ofsignal are first averaged over all regions of interest in step 107, andthen signal averaging within the 12-second section is performed on theresult in step 108, these steps can be performed the other way around.Thus signal averaging for each 12-second section can be conducted foreach region of interest to produce a single representative waveform foreach region of interest. Then the representative waveforms from theplural regions of interest can be averaged to produce a finalrepresentative waveform and corresponding amplitude for that 12-secondsection of signal.

FIG. 4 illustrates results obtained (log of the ratio of ratios of thenormalised blue signal to the normalised red signal) by tracking avolunteer's oxygen saturation in accordance with the invention andsimultaneously using a standard finger-probe pulse oximeter. A 3-CCD(JAIAT-200CL) RGB camera was used and the volunteer was placed in astudy chamber in which the relative concentrations of oxygen, carbondioxide, and nitrogen could be modified so as to induce mild hypoxia andhypercapnia. To produce the results of FIG. 4 the oxygen concentrationsof the chamber were modified by changing the concentration of nitrogenin accordance with the following protocol: the concentrations werelowered so as to induce a change in oxygen saturation in steps of fivepercent (as measured by the reference pulse oximeter) each lasting sevenminutes, from base line oxygen saturation (around 97 percent) to 80percent. Two cycles of fast re-saturations and de-saturations then tookplace through the use of a nasal cannula for oxygen delivery.

As can be seen, the changes in oxygen saturation as measured by themethod of the invention using the video camera shown in FIG. 4(a) trackthe changes in oxygen saturation measured by the reference pulseoximeter in FIG. 4(b) reasonably well.

As mentioned above the calculation made by the invention does notdirectly result in an oxygen saturation value. However in a clinicalsetting oxygen saturation values may be obtained by first calibratingthe system using a standard pulse oximeter. Thus a subject's oxygensaturation can be measured initially (and potentially at intervalsthereafter) using a standard finger-probe pulse oximeter, with thisvalue being used to calibrate the system of the invention, the method ofthe invention then being used primarily to track variations from thatinitial saturation. Significant decreases in oxygen saturation, whichmight represent a worsening of the subject's condition, can be used totrigger an alarm to the clinicians.

Although the main thrust of the invention is to track changes in oxygensaturation, the estimated heart rate and breathing rate used in themethod can be output and displayed on display 7 as additional vitalsigns information.

The invention claimed is:
 1. A method of determining changes in blood oxygen saturation of a subject comprising the steps of: obtaining a video image of an area of exposed skin of the subject, the video image comprising signals representing intensity in at least two different colour channels; defining in the image a plurality of regions of interest in said area of exposed skin of the subject; determining a signal to noise ratio of a heart rate or breathing rate frequency component of one of the colour channel signals or both for each region of interest; determining whether to reject or not reject regions of interest based on the determined signal to noise ratio of a heart rate or breathing rate frequency component or both; processing the colour channel signals from non-rejected regions of interest by: normalising the signal in each of the colour channels by dividing each of the signals by its baseline component; determining the ratio of the amplitudes of the normalised signals from two of the colour channel signals; and outputting changes over time in the ratio as representing changes in blood oxygen saturation of the subject.
 2. The method according to claim 1 wherein the baseline component is the average value of the signal over a predetermined period.
 3. The method according to claim 1 wherein the ratio of the amplitudes of the averaged normalised signals from blue and red colour channel signals are determined.
 4. The method according to claim 1 further comprising the step of averaging each the normalised signals within each colour channel before calculating the ratio of amplitudes.
 5. The method according to claim 4 wherein the step of averaging each of the normalised signals within each colour channel comprises averaging together the signals for that colour channel from each of a plurality of regions of interest within said area of skin of the subject.
 6. The method according to claim 5 wherein in averaging each of the normalised signals within each colour channel the signals from each region of interest are weighted according to the strength of the signal to noise ratio for a heart rate frequency component of one of the colour channel signals from that region of interest.
 7. The method according to claim 4 wherein the step of averaging each of the normalised signals within each colour channel comprises signal averaging to determine a representative waveform for a predetermined time period of said signal.
 8. The method according to claim 7 wherein said signal averaging comprises detecting the times of peaks in one of the colour channel signals, then within each colour channel selecting sections of the normalised signal extending a predetermined time either side of the detected peak times and averaging together the selected sections of the normalised signal.
 9. The method according to claim 1 wherein the area of exposed skin of the subject is selected by detecting areas in the image for which a signal to noise ratio function for the heart rate is maximised.
 10. The method according to claim 1 wherein the regions of interest are formed by dividing the selected area into plural contiguous regions.
 11. The method according to claim 1 wherein regions of interest are rejected if a signal to noise ratio for a heart rate frequency component of a colour channel signal from the region of interest is below a predetermined threshold.
 12. The method according to claim 1 wherein regions of interest are rejected if a signal to noise ratio for a breathing rate frequency component of a colour channel signal from the region of interest is above a predetermined threshold.
 13. The method according to claim 1 wherein regions of interest are rejected if the phase of a heart rate frequency component of a colour channel signal from the region of interest is outside a predetermined threshold of the phase of a heart rate frequency component of the colour channel signal averaged over a plurality of the regions of interest.
 14. The method according to claim 1 wherein the signal in each colour channel is windowed into temporal windows, and the processing steps of normalising and determining are performed for each time window to determine and output a ratio for each time window.
 15. A system for determining changes in blood oxygen saturation of a subject comprising: a video camera for obtaining a video image of an area of exposed skin of the subject, the video image comprising signals representing intensity in at least two different colour channels; a signal processor adapted to receive the signals from the video camera and process them by: normalising the signal in each of the colour channels by dividing each of the signals by its baseline component; defining in the image a plurality of regions of interest in said area of exposed skin of the subject; determining a signal to noise ratio of a heart rate or breathing rate frequency component of one of the colour channel signals or both for each region of interest; determining whether to reject or not reject regions of interest based on the determined signal to noise ratio of a heart rate or breathing rate frequency component or both; determining the ratio of the amplitudes of the normalised signals from two of the colour channel signals for non-rejected regions of interest; and outputting changes over time in the ratio as representing changes in blood oxygen saturation of the subject; and a display adapted to display the changes over time in the ratio. 